6. CONCLUSION AND RECOMMENDATIONS
6.1 Conclusion
The advancement in technology has allowed scientists to acquire, analyse and provide sound conclusions to aid in decision and policy making. This study doesn‟t fall far from this premise. Given the scarcity of water, the planning and management of water resources require information with the highest of accuracy.
The portioning of water across various land covers has to be well understood in order to properly gauge its distribution and use. The response of water over various land cover is different, therefore a baseline land cover is required to assess and compare the use and response of water. Currently the Acocks‟ (1988) Veld Types is used as the baseline in South Africa. However, there has been an improvement in the acquisition and collaboration of land cover information across the country. The SANBI (2012) vegetation map provides a detailed description of the natural vegetation cover of South Africa. However, the water use parameters for this have not been determined. This study investigated a methodology that can be used to determine the ET which is required to determine water use parameters such as crop coefficients.
A review of the various methods to estimate ET (water use) was discussed. There were a number of limitations identified with regard to the in-situ methods to estimate ET. The advent of remote sensing overcomes many of these limitations. Remote sensing models are able to produce large scale estimates of ET at the fraction of the cost compared to in-situ methods.
The ability to acquire ET estimates of the landscape without being in contact with is a value asset especially in large scale studies as such. In South Africa, the SEBAL and SEBS models have been widely applied. For the purpose of this study SEBS was the most suitable model to use. The methodology was separated into two steps to address the first two research questions.
Firstly, in-situ ET data was used to validate satellite derived ET using the SEBS model for several sites across the country to determine its accuracy. The sites were selected based on the availability of ET and meteorological data within each biome. The SEBS ET estimates follow the trend of the in-situ ET data well. The lower ET estimates were produced for the
dry season and higher ET estimates for the wet season. This was the case for the in-situ ET as well. The sites within the Grasslands, Fynbos and Savanna biomes had a number of satellite images available to validate the model with a degree of confidence. R2 values of 0.81, 0.73 and 0.70 were attained for the sites within the Grasslands, Fynbos and Savanna biome, respectively. A regression graph was plotted and the number of data points above the 1:1 line indicate the over-estimation of the model for these sites. The reasons for the over-estimation could be a result of land surface heterogeneity, the variability of air temperature and roughness parameterization as SEBS was initially developed for agricultural landscape.
Although, a number of studies applied SEBS over natural vegetation with a fair degree of accuracy indicating the models ability to estimate ET over natural vegetation. The rest of the in-situ sites did not have sufficient images and this affected the accuracy of the model.
Following the validation of SEBS the model was applied to estimate ET for between 1 July 2014 to 31 June 2015 for the seven biomes. This was done in an attempt to understand the spatial and seasonal variation of ET between the biomes. The modelling results indicated that the Forest biome produced the highest ET in the summer with a mean ET of 4.9 mm.day-
1.This could be the result of the biophysical characteristics (large leaf area index and deep root systems) of the forest vegetation. The lowest ET was produced for the Nama Karoo biome with a mean ET of 0.71 mm.day-1. The scarcity of rain and presence of shrubland vegetation in this area could be the result of this finding. The largest biome in South Africa (Savanna biome) had a mean ET of 2.09 mm.day-1 followed by the Grasslands biome with 3.55 mm.day-1. These biomes have a fair coverage of vegetation and are situated in the wetter region of the country which could attribute to the ET results obtained.
The modelling results highlighted SEBS sensitivity to air temperature. The temperature gradient has a direct impact on the calculation of sensible heat flux, which is used in SEBS to ultimately determine ET. Therefore, the changes in temperature have an influence on ET.
High air temperature results in higher estimates of ET. The winter season experiences low air temperatures compared to the summer season. This trend was present for the ET estimates obtained from SEBS for the month of September, an isolated case, where low air temperature was experienced for a few biomes. This resulted in lower estimates of ET. The models sensitivity to solar radiation could not be expressed as a model was used to determine solar
59
solar radiation gradually increased from the winter to the summer season with no significant fluctuations which would have an impact on the estimation of ET.
This dissertation was aimed at developing a methodology to estimate water use. Given that there are a number of vegetation clusters, this methodology can be applied to derive the water use which will be used to determine the water use parameters as highlighted as the third aim of the WRC project. However, the lack of meteorological data at certain sites will restrict the application of this method. The SEBS model provides a degree of confidence in estimating water use in the Grasslands, Fynbos and Savanna biomes. The model performed well in the Forest biome however, only four images were available. It is recommended that further investigation in the Indian Ocean Coastal Belt, Nama Karoo and Albany Thicket, biome, should be taken to assess the models accuracy to estimate ET.
Satellite derived ET using the SEBS model compared fairly well to in- situ ET in a few biomes. The spatial and temporal resolution of ET can be achieved using remote sensing.
Overall, remote sensing proves a viable option to estimate ET over large areas. The SEBS model is cost-free and Landsat images are freely available. The estimates of ET for this study was produced from across the country without setting up instruments or taking physical measurements. Given that meteorological data is available for the prescribed sites, the estimation of ET is achievable. The use of remote sensing can be used to acquire precipitation, soil moisture and ET data with a degree of accuracy. This is valuable to authorities who require data over larger areas in a short space of time to provide insight in water related issues.
6.2 Limitations and Recommendations
The study can be improved by taking a few factors into consideration. A longer record of in- situ ET data is required to validate against satellite derived ET, especially over the Forest, Indian Ocean Coastal Belt, Nama Karoo and Albany Thicket biomes. Landsat has a temporal resolution of 16 days therefore approximately two images are available per month. For a validation study this is not enough. Cloud cover, especially during the rainy season further reduces the number of satellite images available. For sites with an ET record of two to three weeks, the influence of cloud cover in conjunction with the temporal resolution makes it
difficult to validate. An alternate satellite, for example MODIS, could be used since it has a daily temporal resolution however, the spatial resolution is compromised.
Landsat 7 ETM+ images have scan line which run across the entire image. If this satellite images are used for a validation study it is possible that the location of the in-situ instrument could fall within the scan line which contains no data. This will result in no ET estimation for the site. However, the scan lines are not more than three pixels so the pixel closet to the study site can be used as an alternative.
In-situ ET data for natural vegetation could not be attained for the Azonal and Succulent Karoo biomes. The measurement of ET using in-situ techniques can be conducted which would then provide an opportunity to investigate the accuracy of satellite derived ET from these biomes. The SEBS ET estimates compared better against the ET measurement obtained using a scintillometer than the eddy covariance. This could primarily be related to the larger spatial coverage by the scintillometer. Therefore future studies should consider using the scintillometer to estimate in-situ ET.
The SEBS model requires meteorological data such as wind speed, solar radiation, air temperature and pressure. This data can be obtained using an AWS. For the modelling investigation this data was required from a number of sites therefore the SAWS was selected to provide this data. However, measured solar radiation data was not available and a model by Allen et al. (1998) was used to estimate solar radiation which could have had an influence on the ET estimations. Instruments such as the pyranometer need to be installed at sites to provide actual measurements of solar radiation.
The SEBS model requires a number of inputs which is time consuming to attain especially when dealing with a number of study sites. In some sites not all of the data that is required is available therefore it has to be acquired from other sources. The in-situ data measured at sites are useful for various studies and application however, this data is difficult to access. A database should be developed where measured meteorological, ET or any other important data can be uploaded. This will reduce the amount of time spent searching for data.
61
7. REFERENCES
Acocks, JPH. 1953. Veld Types of South Africa: With 5 small vegetation maps and accompanying Veld Type Map. Botanical Survey Memoir No. 28. The Government
Printer, Pretoria, South Africa.
Acocks, JPH. 1988. Veld Types of southern Africa. Botanical Research Institute, RSA.
Botanical Survey of South Africa Memoirs, 57.
Al-Kaisi, MM and Broner, I. 2009. Crop water use and growth stages. Crop series: Irrigation 4.715:1-3.
Allen, RG, Pereira, LS, Raes, D and Smith, M. 1998. Crop evapotranspiration- Guidelines for computing crop water requirements, FAO Irrigation and drainage Paper No. 56.
Food and Agricultural Organisation of the United Nations, Rome, Italy.
Allen, RG, Tasumi, M and Trezza, R. 2007. Satellite-based energy balance for mapping evapotranspiration with internalized calibration (METRIC)-Model. Journal of Irrigation and Drainage Engineering 133(4): 380-394.
Allen, RG, Pereira, LS, Howell, TA and Jensen, ME. 2011. Evapotranspiration information reporting: I. Factors governing measurement accuracy. Agricultural Water
Management 98:899-920.
Bastiaanssen, WGM, Menenti, M, Feddes, RA and Holtslag, AAM. 1998. A remote sensing surface energy balance algorithm for land (SEBAL) 1. Formulation. Journal of
Hydrology 212-213: 198-212.
Baviaans Tourism. 2012. The Baviaanskloof area.
http://www.baviaans.co.za/page/information_baviaanskloof [Accessed: 10/10/2019].
Badola, A. 2009. Validation of Surface Energy Balance System (SEBS) over forest land cover and sensitivity analysis of the model. MSc. International Institute for Geo Information Science and Earth Observation Enschede, Netherlands.
Bowen, IS. 1926. The ratio of heat losses by conduction and by evaporation from any water surface. Physical Review 27:779-787.
Burger, C. 1999. Comparative evaporation measurements above commercial forestry and sugarcane canopies in the KwaZulu-Natal Midlands. Unpublished MSc. University of Natal, Pietermaritzburg, KwaZulu-Natal, South Africa.
Cook, DR. 2007. Energy balance Bowen ratio. In: Energy Balance Bowen Ratio (EBBR)
Handbook. ARM TR-037. Illinois, USA.
Costa, M.H, Botta, A, Cardille, J.A, 2003. Effects of large-scale changes in land cover on the discharge of the Tocantins River, Southeastern Amazonia. Journal of Hydrology
283:206–217.
Courault, D, Seguin, B and Olioso, A. 2005. Review on estimation of evapotranspiration from remote sensing data: From empirical to numerical modelling approaches.
Irrigation and Drainage Systems 19:223-249.
De Bruin, HAR, Van Den Hurk, BJJ and Kohsiek, W. 1995. The scintillation method tested
over a dry vineyard area. Boundary-Layer Meteorology 76(1):25-40.
Dzikit, S, Jovanovic, NZ, Bugan, R, Israel, S and Le Maitre, DC. 2014. Measurement and modelling of evapotranspiration in three fynbos vegetation types.Water SA (40)2:
189-198.
Department of Water Affairs and Forestry (DWAF). 1999. Stream Flow Reduction Activities: Combined licensing and authorisation guidelines. Pretoria, South Africa.
Elhag, M, Psilovikos, A, Manakos, I and Perakis, K. 2011. Application of the Sebs water balance model in estimating daily evapotranspiration and evaporative fraction from remote sensing data over the Nile Delta. Water Resource Manage 1-12.
Engman, ET and Gurney, RJ. 1991. Remote Sensing in Hydrology. Chapman and Hall,
London, United Kingdom.
Everson, CS. 2001. The water balance of a first order catchment in the montane grasslands of South Africa. Journal of Hydrology 241:110-123.
Everson, C, Clulow, A and Mengistu, M. 2009. Feasibility study on the determination of riparian evaporation in non-perennial systems. Report No. TT 424/09. Water
Research Commission, Pretoria, RSA
Falkenmark, M, Andersson, L, Castensson, R, Sundblad, K, Batchelor, C, Gardiner, J, Lyle, C, Peters, N, Pettersen, B, Quinn, P, Rckström, J, Yapijakis, C, 1999. Water: A reflection of land use. Swedish Natural Science Research Council,Stockholm,
Sweden.
French, AN, Jacob, F, Anderson, MC, Kustas, WP, Timmermans, W. Gieske, A, Su, Z, Su, H, McCabe, MF, Li, F, Prueger, J and Brunsell, N. 2005. Surface energy fluxes with
63
Iowa 2002 SMACEX site (USA). Remote Sensing of Environment 99:55-65.
Fritschen, LJ and Simpson. 1989. Surface energy and radiation balance systems: General description an improvements. American Meteorological Society 28:680-689.
Gibson, LA, Munch, Z and Engelbrecht, J. 2011. Particular uncertainties encountered in using a pre-packaged SEBS model to derive evapotranspiration in a heterogeneous study area in South Africa. Hydrology and Earth System Sciences 15:295-310.
Gibson, LA, Jarmain, C, Su, Z and Eckardt, FE. 2013. Estimating evapotranspiration using remote sensing and the Surface Energy Balance System – A South African
perspective. Water SA (39)4: 477-484.
Google Earth. 2016. United States Department of State Geographer.
Gush, M. 2010. Assessing hydrological impacts of tree-based bioenergy feedstock.
Agricultural Water Management 102:1-7. Gush, M. 2010. Assessing hydrological impacts of tree-based bioenergy feedstock. In ed. Amezaga, JM, van Maltitz, G and Boyes, S. Assessing the Sustainability of Bioenergy Projects in Developing Countries: A framework for policy evaluation, Ch. 3, 37-52. LAW Printing,
RSA.
Gush, B and Dye, PJ. 2015. Water use and socio-economic benefit of the biomass of indigenous trees Volume 2: Site specific technical report. Report No. 1876/2/15.
Water Research Commission, Pretoria, RSA.
Hill, RJ. 1992. Review of optical scintillation methods of measuring the refractive-index spectrum, inner scale and surface fluxes. Waves in Random Media 2:179-201.
Huang, C, Li, Y, Gu, J, Lu, L, Li, X. 2015. Improving estimation of evapotranspiration under water-limited conditions based on SEBS and MODIS data in arid regions. Remote Sensing 7:16795-16814.
Irmak, S. 2009. Crop coefficient values. [Internet]. University of Nebraska – Lincoln Extension, Lincoln, United States of America. Available from:
http://www.ianrpubs.unl.edu/pages/publicationD.jsp?publicationId=1237. [Accessed:
26/03/15].
Jarmain, C, Everson, CS, Savage, MJ, Mengistu, MG, Clulow, AD, Walker, S and Gush, MB. 2009a. Refining tools for evaporation monitoring in support of water resources management. Report No. 1567/1/08. Water Research Commission, Pretoria, RSA.
Jarmain, C, Bastiaanssenm, W, Mengistu, MG, Jewitt, G and Kongo, V. 2009b. A
methodology for near-real time spatial estimation of evaporation. Report No.
1751/1/09. Water Research Commission, Pretoria, RSA.
Jewitt, GPW, Lorentz, SA, Gush, MB, Thornton-Dibb, S, Kongo, V, Wiles, L, Blight, J, Stuart-Hill, SI, Versfeld, D and Tomlinson, K. 2009. Methods and guidelines for the licensing of SFRAs with particular reference to low flows. Report No. 1428/1/09.
Water Research Commission, Pretoria, RSA.
Jin, X, Guo, R and Xia, W. 2013. Distribution of actual evapotranspiration over Qaidam Basin, an arid area in China. Remote Sensing 5: 6976-6996.
Jovanovic, N and Isreal, S. 2012. Critical review of methods for the estimation of actual evapotranspiration in hydrological models. In: ed Irmak, A, Evapotranspiration
Remote Sensing and Modelling, Ch. 15, 329-350. InTech, Rijeka, Croatia.
Knight, F. 2012. Agricultural Assessment of Baviaanskloof. Agri Informatics Development
Trusts. Durbanville, South Africa.
Li, Z, Tang, R, Wan, Z, Bi, Y, Zhou, C, Tang, B, Yan, G and Zhang, X. 2009. A review of current methodologies for regional evapotranspiration estimation from remotely
sensed data. Sensors 9:3801-3853.
Liou, YA and Kar, SK. 2014. Evapotranspiration estimation with remote sensing and various surface energy balance algorithms- A Review. Energies 7:2821-2849.
Ma, W, Hafeez, M, Rabbani, U, Ishikwa, H and Ma, Y. 2012. Retrieved actual ET using SEBS model from Landsat-5 TM data for irrigation area of Australia.
Mamo, TA. 2010. Estimation of actual evapotranspiration and water balance using combined geostationary and polar orbiting satellite products: A Case Study in Spain. MSc University of Twente – ITC, Enschede, 1-86.
Meijninger, WML and Jarmain, C. 2014. Satellite-based annual evaporation estimates of invasive alien plant species and native vegetation in South Africa. Water SA 40:95
108.
Meijninger, WML, Hartogensis, OK, Kohsiek, W, Hoedjes, JCB, Zuurbier, RM and De Bruin, HAR. 2002. Determination of area-averaged sensible heat fluxes with large aperture scintillometer over a heterogeneous surface- Flevoland field experiment. Boundary Layer Meteorology 105:37-62.
Mengistu, MG, Everson, CS, Moyo, NC and Savage, MJ. 2014. The validation of the
65
variables (evaporation and soil moisture) in hydrometeorological models. Report No.
2066/1/13. Water Resource Commission, Pretoria, South Africa.
Mengistu, MG. 2008. Heat and energy exchange above different surfaces using Surface Renewal. Ph.D. thesis. University of KwaZulu-Natal, Pietermaritzburg, South Africa.
Mengistu, MG and Savage, MJ. 2010. Surface renewal method for estimating sensible heat
flux. Water SA 36(1):9-18.
Meyers, TP and Baldocchi, DD. 2005. Current micrometeorological flux methodologies with applications in agriculture. Micrometeorology in Agricultural Systems, Agronomy
Monograph 47:381-396.
Mkhwanazi, MM and Chavez, JL. 2013. Mapping evapotranspiration with the remote sensing ET algorithms METRIC and SEBAL under advective and non-advective conditions:
accuracy determination with weighing lysimeters. Hydrology Days 1-6.
Moran, MS, Clarke, TR, Inoue, Y and Vidal, A. 1994. Estimating crop water deficit using the relation between surface-air temperature and spectral vegetation index. Remote
Sensing of Environment 49(3): 246-263.
Mucina, L and Rutherford, MC. 2006. The vegetation of South Africa, Lesotho and Swaziland. Strelitzia 19. South African National Biodiversity Institute, Pretoria, South
Africa.
National Aeronautics and Space Administration (NASA). 2010. Landsat 7 science data
user’s handbook. USA.
National Water Act (NWA). 1998. Act No. 36 of 1998. Government Printer, Pretoria, South
Africa.
Paw U, KT and Brunet, Y. 1991. A surface renewal measure of sensible heat flux density. 20th Conference on Agricultural and Forest Meteorology,52–53. American Meteorological
Society Boston,USA.
Paw U, KT, Qiu, J, Su, HB, Watanabe, T, Brunet,Y. 1995. Surface renewal analysis: A new method to obtain scalar fluxes without velocitydata. Agricultural and Forest
Meteorology 74: 119–137.
Ramoelo, A, Majozi, N, Mathieu, R, Jovanovic, N, Nickless, A and Dzikiti, S. 2014.
Validation of the global evapotranspiration product (MOD16) using flux tower data in the African Savanna, South Africa. Remote Sensing 6:7406-7423.
Reynolds, O. 1895. On the dynamical theory of incompressible viscous fluids and the determination of the criterion. Philosophical Transactions of the Royal Society of London 186:123-164.
Rwasoka, DT, Gumindoga, W and Gwenzi, J. 2011. Estimation of actual evapotranspiration using the Surface Energy Balance System (SEBS) algorithm in the Upper Manyame catchment in Zimbabwe. Physics and Chemistry of the Earth 36: 736-746.
Savage, MJ, Everson, CS, Metelerkamp, BR. 1997. Evaporation measurement above vegetated surfaces using micro-meteorological techniques. Report No. 349/1/97.
Water Research Commission Report, Pretoria, South Africa
Savage, MJ, Everson CS, Odhiambo GO, Mengistu MG and Jarmain C. 2004. Theory and practice of evapotranspiration measurement, with special focus on SLS as an operational tool for the estimation of spatially-averaged evaporation. Report No.
1335/1/04. Water Research Commission, Pretoria, South Africa.
Savage, MJ, Odhiambo, Mengistu, MG, Everson, CS and Jarmain, C. 2010. Measurement of grassland evaporation using a surface-layer scintillometer. Water SA 36(1):1-8.
Schulze, R.E. 2003. Modelling as a Tool in Integrated Water Resources Management:
Conceptual Issues and Case Study Applications. Report No. 749/1/04 Water Research
Commission, Pretoria, South Africa.
Schulze, RE and Pike, A. 2004. Development and evaluation of an installed hydrological modelling system. Report No. 1155/1/04. Water Research Commission, Pretoria,
South Africa.
Schulze, R.E. 2007. Baseline Land Cover. In: Schulze, R. E. South African Atlas of Climatology and Agrohydrology. Report No. 1489/1/06. Water Research Commission,
Pretoria, South Africa.
Shoko, C, Clark, D, Mengistu, M, Dube, T and Bulcock, H. 2015b. Effect of spatial resolution on remote sensing estimation of total evaporation in the uMngeni catchment, South Africa. Journal of Applied Remote Sensing. 9:1-22.
Singh, KR and Senay, G. 2016. Comparison of four different energy balance models for estimating evapotranspiration in the Midwestern United States. Water 8(9):1-19.
Snyder, P, Spano, D and Paw U, KT. 1995. Surface renewal analysis for sensible and latent heat flux density. Boundary-Layer Meteorology 77:249-266.